Modelling and Analysis of the Distributed Coordination Function of IEEE 802.11 with Multirate Capability

Abstract

Abstract-The aim of this paper is twofold. On one hand, it presents a multi-dimensional Markovian state transition model characterizing the behavior at the Medium Access Control (MAC) layer by including transmission states that account for packet transmission failures due to errors caused by propagation through the channel, along with a state characterizing the system when there are no packets to be transmitted in the queue of a station (to model non-saturated traffic conditions). On the other hand, it provides a throughput analysis of the IEEE 802.11 protocol at the data link layer in both saturated and non-saturated traffic conditions taking into account the impact of both transmission channel and multirate transmission in Rayleigh fading environment. Simulation results closely match the theoretical derivations confirming the effectiveness of the proposed model.

On the Throughput Allocation for Proportional Fairness in Multirate IEEE 802.11 DCF

“…As a starting point for the derivations which follow, we adopt the bi-dimensional model proposed in [4], appropriately modified in order to account for the following scenario. In the investigated network, each station employs a specific bit rate, R (s) d , a different transmission packet rate, λ s , transmits packets with size P L (s) , and it employs a minimum contention window with size W (s) 0 , which can differ from the one specified in the IEEE 802.11 standard [1] (this is required for optimizing the aggregate throughput while guaranteeing fairness among the contending stations).…”

“…Furthermore, each station is identified by an index s ∈ {1, · · · , N}, and it belongs to a unique duration-class. In order to identify the class of a station s, we define With this setup, the probability that the s-th station starts a transmission in a randomly chosen time slot is identified by τ s , and it can be obtained by solving the bidimensional Markov chain for the contention model of the s-th station [4]: are, respectively, the collision and the packet error probabilities related to the s-th station. Given τ s in (1), we can evaluate the aggregate throughput S as follows:…”

“…In order to greatly simplify the analysis, we consider small queue (i.e., KQ → 1, where KQ is the queue size expressed in number of packets). In order to account for the station traffic, the Markov model employs two probabilities, q and P I,0 , defined as in [4]. Briefly, non-saturated traffic is accounted for by a new state labelled I, which considers the following two situations: 1) After a successful transmission, the queue of the transmitting station is empty.…”

“…The scenario at hand requires a number of modifications to the previous theoretical results derived in [4]. First of all, given the payload lengths and the data rates of the N stations, the N c duration-classes are arranged in order of decreasing durations identified by the index d ∈ {1, • • • , N c }, whereby d = 1 identifies the slowest class.…”

“…After the landmark work by Bianchi [3], who provided an analysis of the saturation throughput of the basic 802.11 protocol assuming a two dimensional Markov model at the MAC layer, many papers have addressed almost any facet of the behaviour of DCF in a variety of traffic loads and channel transmission conditions (see [4]- [6] and references therein). A current topic of interest in connection to DCF regards the allocation of throughput in order to mitigate the performance anomaly, while ensuring fairness among multirate stations.…”